Download Fuzzy Approach to pH Signal improvement to Reduce Process

Fuzzy Approach to pH Signal improvement to
Reduce Process Pickup of Disturbances
Dr.Ramesh Patnaik1, Prof Rajesh kanuganti*2,
#1
Department of Instrument Technology, Andhra University
A.U.C.E, Visakhapatnam , A.P, India
[email protected]
#2
Assistant Professor ,Dept of ECE Khammam Institute of Technology & sciences
Khammam, A.P,INDIA
[email protected]
Abstract—pH is a deceptively simple measurement.
However, there are many factors that need to be taken
into account for a reliable reading. The most important
characteristic of pH electrodes is it's very high
impedance, of the order of 109 ohms. This is
compounded by noisy factory environments and by long
distances between the electrode and the controller. A
typical pH measuring device would be normally
configured to operate in the single ended mode, also
known as the asymmetrical mode. This means that the
reference electrode would be connected to the ground
potential of the amplifier. This configuration works
very well as long as the environment is electronically
noise-free. This is not the situation in an industrial
environment. It is very commonly seen that the readings
on a pH controller suddenly fluctuates, even to overrange or under-range condition. In this paper the
application of fuzzy logic in combination with smart
transmitter network is investigated to reduce the signal
wandering due to various disturbing signals.
Key words: pH, combined electrode, signal wandering,
smart transmitter, fuzzy logic
I. INTRODUCTION
A. PH Measurement
The concept of the pH measurement start as the
negative logarithm of the hydrogen ion concentration
The glass pH electrode is still the most prevalent
online composition measurement in the process
industry. The logarithmic relationship offers an
incredible rangeability and sensitivity far beyond the
capability of any other common field measurements.
For the 0-14 pH scale, the glass electrode can
measure 14 orders of magnitude change in hydrogen
ion concentration. At 7 pH, the glass electrode can
detect changes in the hydrogen ion concentration to
the eight decimal place.
aH = 10−pH
pH = − log (aH)
aH = γ ∗ cH
cH ∗ cOH = 10−pKw
(1)
(2)
(3)
(4)
Where:
aH = hydrogen ion activity (gmmoles per liter)
cH = hydrogen ion concentration
(gm-moles per liter)
cOH = hydroxyl ion concentration
(gm-moles per liter)
γ = activity coefficient (1 for dilute
solutions)
pH = negative base 10 power of
hydrogen ion activity
pKw = negative base 10 power of
the water dissociation constant (14.0 at
25oC)
More exactly pH is a function of hydrogen ion
activity as defined by Equations 1 and 2. Most pH
measurements are in extremely dilute water solutions
where the hydrogen ion concentration is the same as
the hydrogen ion activity. In Equation 3, this one to
one relationship corresponds to a unity activity
coefficient. In some chemical reactors, the
concentration of ions or non-aqueous solvents is high
enough to cause the activity coefficient to
significantly decrease. In these applications, the pH
can change even if the hydrogen ion concentration is
constant if the concentration of ions and solvent
changes. The product of the hydrogen and hydroxyl
ion concentration in a water solution depends upon
the water dissociation constant (pKw) per Equation 4.
The water dissociation constant changes with
temperature on the average about -0.03 per deg C
which for a given hydroxyl ion concentration causes
the pH to change -0.03 per deg C for strong basic
solutions. This change in solution pH with
temperature is not corrected by the standard
temperature compensator. A 8.3 pH hot waste stream
at 50 OC that cools down to 25 OC by the time it
reaches the effluent discharge will be above 9 pH,
possibly in violation of an environmental constraint.
The most popular pH sensor in chemical and
waste treatment processes is the combination
electrode that consists of a glass measurement
electrode surrounded by a reference electrode. A
spherical or hemi-spherical glass bulb has a pH
sensitive surface that develops a milliVolt potential
(E1) that is proportional to pH of the process. The
inside surface of the glass electrode also develops a
potential that corresponds to a 7 pH buffer. The
sensing of pH depends upon a thin hydrated layer on
the glass surface. If the surface in contact with
process is in the same condition as the surface on the
interior of the bulb, the difference in the milliVolt
potentials between the interior and exterior glass
surfaces can be described by the Equation 5, which is
a simplification of the Nernst Equation. The standard
temperature compensator corrects for the change in
the milliVolts generated per pH unit by the
temperature factor in Equation 5. Typically an RTD
sensor embedded in the electrode is used to provide
automatic compensation that effectively keeps the
temperature factor at 298 oK (25 oC) in Equation 5.
At 25 oC we have Equation 6 with the often stated
potential for the glass measurement electrode of
−59.16 milliVolts/pH. If you plot pH versus
milliVolts per Equation 6, the intercept is at 7 pH and
0 milliVolts. The slope is −59.16 milliVolts, which
corresponds to 100% efficiency. As the pH electrode
ages, the slope and efficiency decreases.
There must also be a reference electrode
whose internal electrolyte is in contact with the
process to complete the circuit through the process
liquid. For the combination electrode shown in
Figure 1 there is concentric porous ring called a
“liquid junction” around the glass bulb for the
potassium chloride reference electrolyte to move
through to reach the process.
E1 – E2 = 0.1984*(T + 273)*(7 − pH1)
(5)
at T =
25 oC:
E1 − E2 = −59.16 ∗ (pH1 − 7)
(6)
Figure 1. Combination pH electrode –
The potential (E1) developed at the glass electrode
surface is indicative of the process pH. Changes in
the hydrated glass surface layer activity and shunt
resistance (R11) are corrected by a slope calibration
adjustment. Changes in the liquid junction potential
(E5) are corrected by a zero (offset) adjustment.
B.Fuzzy logic
Fuzzy logic has two different meanings. In a
narrow sense, fuzzy logic is a logical system, which
is an extension of multi-valued logic. But in a wider
sense, fuzzy logic is almost synonymous with the
theory of fuzzy sets, a theory which relates to classes
of objects with un-sharp boundaries in which
membership is a matter of degree. The concept of
fuzzy logic is very different both in spirit and
substance from the concepts of traditional multi
valued logical systems. What is added is that the
basic concept underlying FL is that of linguistic
variable, that is, a variable whose values are words
rather than numbers. In effect, much of FL may be
viewed as a methodology for computing with words
rather than numbers. Although words are inherently
less precise than numbers, their use is closer to
human intuition. Furthermore, computing with words
exploits the tolerance for imprecision and thereby
lowers the cost of solution.
The traditional deterministic set in a universum
can be represented by the characteristic function
mapping
into two-element set
,
namely for
if
, and
if
.
A fuzzy subset
membership function
unit interval
of
is defined by a
mapping
into a closed
, where for
if
,
if
, and
if possibly belongs to
but
it is not sure.
For the last case - the nearer to 1 the value
is, the higher is the possibility that
.
In effect, much of FL may be viewed as a
methodology for computing with words rather than
numbers. Although words are inherently less precise
than numbers, their use is closer to human intuition.
Furthermore, computing with words exploits the
tolerance for imprecision and thereby lowers the cost
of solution.
C.Smart transmitters
The benefits of using smart transmitter network in the
present configuration are of two fold. It automates the
entire process at an attractive cost while providing
more effective communication within the control
system .The smart technology will enable us to utilize
its advantages for initial start-up, maintenance work
and to change the settings. All parameters for new
measuring points or those to be changed are already
defined and entered in the office. The stored data
only need to be imported to the measuring instrument
and the measuring point started up from the control
room. The same applies to the operation and
maintenance. The status of the measuring instrument
can be displayed on-line, or in test mode the current
output can for example, be set to a specific value in
order to test the whole circuit. Most importantly the
communications capabilities of the smart transmitters
are utilized in the present system for the convenience
of implementation of the logic.
II PROBLEMS IN
MEASUREMENT:
CONVENTIONAL
PH
A.Electrical Interference –D.C sources
pH is a deceptively simple measurement.
However, there are many factors that need to be
taken into account for a reliable reading. The most
important characteristic of pH electrodes is it's very
high impedance, of the order of 10 9 ohms. This is
compounded by noisy factory environments and by
long distances between the electrode and the
controller.
A typical pH measuring device would be normally
configured to operate in the single ended mode, also
known as the asymmetrical mode. This means that
the reference electrode would be connected to the
ground potential of the amplifier. This configuration
works very well as long as the environment is
electronically noise-free. This is not the situation in
an industrial environment. It is very commonly seen
that the readings on a pH controller suddenly
fluctuates, even to over-range or under-range
condition. This situation arises, when for example,
the mixing motor is switched on. An old leaky motor
might inject some electrical interference of 1 to 2
volts into the liquid whose pH is monitored. This
noise being a common signal, is picked up by both
the pH and the reference electrodes. Since in the
asymmetrical mode, the reference electrode is
grounded, the electrical noise is present only on the
pH electrode. This noise would be amplified along
with the pH signal and thus the fluctuating readings.
If the electrical noise was from a DC source,
typically like those in an electroplating tank, the
problem would not be fluctuating readings mostly
stable but incorrect values.
B .Effect of mixing water flow rate:
The dosing of the chemical would not be regulated
based on the deviation of the pH from the set point
but at a steady and fixed rate. This would cause
overshoot and undershoot of the process and hence
the control will not be smooth. In applications where
fine control is required like those in food or
pharmaceutical applications which usually operate
within a narrow band, a limit control would not be
acceptable. In addition the delay in ionization and
spreading of reading affects the pH reading and by
incorporating the mixing water flow rate into fuzzy
logic compensation.
C.Tank disturbance pick up
In certain industries it becomes essential to monitor
two parameters simultaneously and carry out
corrective action based on one parameter first
followed by the other.
D. Effect of temperature variation
Most pH measurements are in extremely dilute
water solutions where the hydrogen ion concentration
is the same as the hydrogen ion activity. IIn these
applications, the pH can change even if the hydrogen
ion concentration is constant if the concentration of
ions and solvent changes.The product of the
hydrogen and hydroxyl ion concentration in a water
solution depends upon the water dissociation constant
(pKw).
control room. The transmitters can be configured
with the help of PC. However it requires a smart to
RS232 converter to access the transmitters. In
addition, the driver software has to be installed in the
computer system.
The data communicated to the PC is used as the
raw material for the fuzzy processing algorithm. The
logic is resident in the system and implemented with
the help of Matlab program.
The water dissociation constant changes with
temperature on the average about -0.03 per OC which
for a given hydroxyl ion concentration causes the pH
to change -0.03 per OC for strong basic solutions.
This change in solution pH with temperature is not
corrected by the standard temperature compensator.
III.FUZZY APPROACH
A.Basic configuration
C. Implementation of communications
The interface for the smart system is the current
output. Digital information can be transmitted
simultaneously both ways via the current output
cables. The current output signal (0/4 to 20mA) is not
affected because the mean value of the signal
containing the digital information is equal to zero.
The signals are superimposed by means of frequency
shift keying(FSK) based on the standard 202Bell.The
digital transmission signal is formed from two
frequencies: 2200Hz="0" and 1200Hz="1"
B. Hardware scheme
The system utilized four smart transmitters for
mixing water flow rate, recognised D.C source
effects, temperature changes and the tank disturbance
pickups. The transmitters work on fsk digital signals
superimposed on the 4-20mA current loop. The
settings of the transmitters are performed with the
help of a smart communicator. The communicator
can be fixed anywhere on the loop at field or at the
In addition to the Normal point-to-point link, smart
system also enables a network to be configured with
up to 15 field devices. The cycle time is in the range
of 10 seconds, which thus only allows limited
application. Also, the analog output can no longer be
used. The data transmission layer defines the format
of a telegram. Access is to the master/slave method.
The master station is in all cases the hand-held
controller. Two masters can be connected up
simultaneously.
C.Fuzzy logic implementation.
The behaviour of the effecting disturbances were
studied over a period of time and also when the
operators experience was combined, it was found that
the input functions were most closely matching with
those depicted in the table 1. The selected rules are
given as per the table 3. All the output member ship
functions were taken as trimf for simplicity. The
fuzzy algorithm was taken of Mamdani type.
Fuzzy model:
Surface (for one set of two variables)
TABLE I
INPUT MEMBERSHIP FUNCTIONS
1
Mixing
florate
water
Low
Normal
High
Gaussmf
Gauss2mf
gaussmf
2
DC
disturbance
signals
3
Temperature
variations
4
Tank disturbances
Low
Normal
High
Low
Normal
High
Low
Normal
High
Gaussmf
Gauss2mf
gaussmf
Gbellmf
gaussmf
gbellmf
Gbellmf
gaussmf
gbellmf
IV.RESULTS AND DISCUSSSION
TABLE2
OUT PUT MEMBERSHIP FUNCTIONS
1
Correction for pH
signal
Rules view
Low
Normal
High
Trimmf
Trimfmf
Trimmf
A. Transmitter
trimming
performance
without
fuzzy
The plot shows some irregular and indefinite spikes
and flat portions which are infact erratic readings
which are received without fuzzy trimming.
error plot (Y-axis time, min, Y-axis pH error)
B.System with fuzzy trimming
Simultaneous plot was taken with fuzzy trimming
which is shown below. The disturbing spikes are
reduced by this approach as can be observed from the
below plot. The improvements were marked in the
plot.
V.CONCLUSIONS
Fuzzy logic trimming of the pH signal proposes
an alternative method of signal improvement
where the disturbances of a system cannot be
truly defined in mathematical terms. The
resultant
system
was
shown
marked
improvements as shown in the results section.
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